1. Background and Relevant Art
Computer systems and related technology affect many aspects of society. Indeed, the computer system's ability to process information has transformed the way we live and work. Computer systems now commonly perform a host of tasks (e.g., word processing, scheduling, and database management) that prior to the advent of the computer system were performed manually. More recently, computer systems have been coupled to one another and to other electronic devices to form both wired and wireless computer networks over which the computer systems and other electronic devices can transfer electronic data. As a result, many tasks performed at a computer system (e.g., voice communication, accessing electronic mail, controlling home electronics, Web browsing, and printing documents) include the exchange of electronic messages between a number of computer systems and/or other electronic devices via wired and/or wireless computer networks.
However, to utilize a network resource to perform a computerized task, a computer system must have some way to identify and access the network resource. Accordingly, resources are typically assigned unique identifiers, for example, network addresses, that uniquely identify resources and can be used to distinguish one resource from other resources. Thus, a computer system that desires to utilize a resource can connect to the resource using the network address that corresponds to the resource. However, accessing a network resource can be difficult if a computer system has no prior knowledge of a network address for a network resource. For example, a computer system can not print a document at a network printer unless the computer system (or another networked computer system) knows the network address of the network printer.
Accordingly, various mechanisms (e.g., Domain Name System (“DNS”), Active Directory (“AD”), Distributed File Systems (“DFS”)) have been developed for computer systems to identify (and access) previous unknown resources. However, due to the quantity and diversity of resources (e.g., devices and services) that are accessible via different computer networks, developers are often required to develop applications that implement a variety of different resource identification and access mechanisms. Each different mechanism may have different coding requirements and may not provide a developer with all the functionality that is needed in an application.
For example, although DNS has a distributed administration architecture (i.e., centralized management is not required), DNS is not sufficiently dynamic, not self-organizing, supports a weak data and query model, and has a fixed set of roots. On the other hand, AD is sufficiently dynamic but requires centralized administration. Further, aspects of different mechanisms may not be compatible with one another. For example, a resource identified using DNS may not be compatible with DFS routing protocols. Thus, a developer may be forced to choose the most suitable mechanism and forgo the advantages of other mechanisms.
Mechanisms for identifying resources can be particularly problematic in peer-to-peer networks. DNS provides a lookup service, with host names as keys and IP addresses as values, that relies on a set of special root servers to implement lookup requests. Further, DNS requires management of information (NS records) for allowing clients to navigate the name server hierarchy. Thus, a resource must be entered into DNS before the resource can be identified on a network. On larger scale networks where nodes frequently connect and disconnect form the network relying on entry of information is not always practical. Additionally, DNS is specialized to the task of find hosts or services and is not generally applicable to other types of resources.
Accordingly, other mechanisms for resource identification and access have been developed to attempt to address these shortcomings. A number of mechanisms include distributed lookup protocols that are more scalable than DNS. These mechanisms use various node arrangements and routing algorithms to route requests to corresponding resources and to store information for lookup.
At least one of these mechanisms utilizes local multi-level neighbor maps at each node in a network to route messages to a destination node. This essentially results in an architecture where each node is a “root node” of a corresponding tree of nodes (the nodes in its neighbor map). Messages are incrementally routed to a destination ID digit by digit (e.g., ***6=>**46=>, *346=>2346, where *s represent wildcards). The routing efficiency of these types of mechanisms is O(log N) routing hops and require nodes to maintain a routing table of O(log N) size.
At least one other of these mechanisms assigns nodes a unique ID that is taken from a linear ring of numbers. Nodes maintain routing tables that contain pointers to their immediate successor node (according to ID value) and to those nodes whose ID values are the closest successor of the value ID+2L. The routing efficiency of these types of mechanisms is also O(log N) routing hops and require nodes to maintain a routing table of O(log N) size.
At least one further mechanisms requires O(log N1/d) routing hops and requires nodes to maintain a routing table of O(D) size. Thus, the routing efficiency of all of these mechanisms depends, at least in part, on the number of nodes in the system.
Further, since IDs (for at least some of the mechanisms) can be uniformly distributed around a ring, there is always some possibility that routing between nodes on the ring will result in some inefficiency. For example, routing hops can cross vast geographic distances, cross more expensive links, or pass through insecure domains, etc. Additionally, when message routing involves multiple hops, there is some chance that such events will occur multiple times. Unfortunately, these mechanisms do not take into account the proximity of nodes (physical or otherwise) with respect one another. For example, depending on node distribution on a ring, routing a message from New York to Boston could involve routing the message from New York, to London, to Atlanta, to Tokyo, and then to Boston.
Accordingly, at least one other more recent mechanism takes proximity into account by defining proximity as a single scalar proximity metric (e.g., IP routing hops or geographic distance). These mechanisms use the notion of proximity-based choice of routing table entries. Since there are many “correct” node candidates for each routing table entry, these mechanisms attempt to select a proximally close node from among the candidate nodes. For these mechanisms can provide a function that allows each node to determine the “distance” of a node with a given IP address to itself. Messages are routed between nodes in closer proximity to make progress towards a destination before routing to a node that is further away. Thus, some resources can be conserved and routing is more efficient.
Unfortunately, these existing mechanisms typically do not provide for, among other things, symmetric relationships between nodes (i.e., if a first node considers a second node to be its partner, the second node considers the first node as a partner as well), routing messages in both directions (clockwise and counterclockwise) on a ring, partitioning linked lists of nodes based on a plurality of proximity metrics, and routing messages based on a plurality of proximity metrics. These deficiencies can limit dynamic, distributed, and efficient transfer of data between nodes of a network, such as, for example, when broadcasting data to all nodes of the network.
In some environments, safety mechanisms are used to insure that node responsibilities do not inappropriately overlap. For example, a safety mechanism can be used to prevent two different nodes from claiming responsibly for a system resource (e.g., a message) or logical identity within that system. In some environments, liveness mechanisms are used to insure that if a message is repeatedly sent to a target the message is accepted. Unfortunately, many existing asynchronous systems provide only limited safety and liveness mechanisms. For example, some asynchronous systems provide only eventually safety and liveness. Thus, these asynchronous systems are not suitable for various types of applications, such as, for example, authoritative storage.